epsilon-Entropy and the Complexity of Feedforward Neural Networks. - Robert C. Williamson (NIPS, 1990)

Probabilistic arithmetic. I. Numerical methods for calculating convolutions and dependency bounds. - Robert C. Williamson; Tom Downs (Int. J. Approx. Reason., 1990)

Probabilistic arithmetic - Robert C. Williamson (1990)

Investigating the Distribution Assumptions in the Pac Learning Model. - Peter L. Bartlett; Robert C. Williamson (COLT, 1991)

The circular nature of discrete-time frequency estimates. - Brian C. Lovell; Peter J. Kootsookos; Robert C. Williamson (ICASSP, 1991)

Splines, Rational Functions and Neural Networks. - Robert C. Williamson; Peter L. Bartlett (NIPS, 1991)

An extreme limit theorem for dependency bounds of normalized sums of random variables. - Robert C. Williamson (Inf. Sci., 1991)

Rational Parametrizations of Neural Networks. - Uwe Helmke; Robert C. Williamson (NIPS, 1992)

The statistical performance of some instantaneous frequency estimators. - Brian C. Lovell; Robert C. Williamson (IEEE Trans. Signal Process., 1992)

The relationship between instantaneous frequency and time-frequency representations. - Brian C. Lovell; Robert C. Williamson; Boualem Boashash (IEEE Trans. Signal Process., 1993)

Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes. - Wee Sun Lee; Peter L. Bartlett; Robert C. Williamson (COLT, 1994)

A simple calculation of the joint moments of hidden Markov models. - Mehmet Karan; Brian D. O. Anderson; Robert C. Williamson (ICASSP (4), 1994)

Threshold effects in multiharmonic maximum likelihood frequency estimation. - Robert C. Williamson; Ben James; Brian D. O. Anderson; Peter J. Kootsookos (Signal Process., 1994)

Conditional mean and maximum likelihood approaches to multiharmonic frequency estimation. - Ben James; Brian D. O. Anderson; Robert C. Williamson (IEEE Trans. Signal Process., 1994)

Performance of the maximum likelihood constant frequency estimator for frequency tracking. - Mehmet Karan; Robert C. Williamson; Brian D. O. Anderson (IEEE Trans. Signal Process., 1994)

On Efficient Agnostic Learning of Linear Combinations of Basis Functions. - Wee Sun Lee; Peter L. Bartlett; Robert C. Williamson (COLT, 1995)

Learning curves from a modified VC-formalism: a case study. - Adam Kowalczyk; J. Szymariski; Robert C. Williamson (ICNN, 1995)

Examples of learning curves from a modified VC-formalism. - Adam Kowalczyk; Jacek Szymanski; Peter L. Bartlett; Robert C. Williamson (NIPS, 1995)

Neural networks, rational functions, and realization theory. - Uwe Helmke; Robert C. Williamson (Math. Control. Signals Syst., 1995)

Lower Bounds on the VC Dimension of Smoothly Parameterized Function Classes. - Wee Sun Lee; Peter L. Bartlett; Robert C. Williamson (Neural Comput., 1995)

Existence and uniqueness results for neural network approximations. - Robert C. Williamson; Uwe Helmke (IEEE Trans. Neural Networks, 1995)

Characterization of threshold for single tone maximum likelihood frequency estimation. - Ben James; Brian D. O. Anderson; Robert C. Williamson (IEEE Trans. Signal Process., 1995)

An efficient calculation of the moments of matched and mismatched hidden Markov models. - Mehmet Karan; Brian D. O. Anderson; Robert C. Williamson (IEEE Trans. Signal Process., 1995)

A Framework for Structural Risk Minimisation. - John Shawe-Taylor; Peter L. Bartlett; Robert C. Williamson; Martin Anthony (COLT, 1996)

Nearfield broadband frequency invariant beamforming. - Rodney A. Kennedy; Thushara D. Abhayapala; Darren B. Ward; Robert C. Williamson (ICASSP, 1996)

Fat-Shattering and the Learnability of Real-Valued Functions. - Peter L. Bartlett; Philip M. Long; Robert C. Williamson (J. Comput. Syst. Sci., 1996)

The VC Dimension and Pseudodimension of Two-Layer Neural Networks with Discrete Inputs. - Peter L. Bartlett; Robert C. Williamson (Neural Comput., 1996)

FIR filter design for frequency invariant beamformers. - Darren B. Ward; Rodney A. Kennedy; Robert C. Williamson (IEEE Signal Process. Lett., 1996)

Efficient agnostic learning of neural networks with bounded fan-in. - Wee Sun Lee; Peter L. Bartlett; Robert C. Williamson (IEEE Trans. Inf. Theory, 1996)

A PAC Analysis of a Bayesian Estimator. - John Shawe-Taylor; Robert C. Williamson (COLT, 1997)

An adaptive algorithm for broadband frequency invariant beamforming. - Darren B. Ward; Rodney A. Kennedy; Robert C. Williamson (ICASSP, 1997)

Online learning via congregational gradient descent. - Kim L. Blackmore; Robert C. Williamson; Iven M. Y. Mareels; William A. Sethares (Math. Control. Signals Syst., 1997)

Correction to 'Lower Bounds on the VC-Dimension of Smoothly Parametrized Function Classes'. - Wee Sun Lee; Peter L. Bartlett; Robert C. Williamson (Neural Comput., 1997)

Sampling rate versus quantisation in speech coders. - Jennifer A. Fulton; Robert R. Bitmead; Robert C. Williamson (Signal Process., 1997)

Decision region approximation by polynomials or neural networks. - Kim L. Blackmore; Robert C. Williamson; Iven M. Y. Mareels (IEEE Trans. Inf. Theory, 1997)

Broadband beamforming using elementary shape invariant beampatterns. - Thushara D. Abhayapala; Rodney A. Kennedy; Robert C. Williamson (ICASSP, 1998)

Shrinking the Tube: A New Support Vector Regression Algorithm. - Bernhard Schölkopf; Peter L. Bartlett; Alexander J. Smola; Robert C. Williamson (NIPS, 1998)

On the Relationship Between Behavioural and Standard Methods for System Identification. - Erik Weyer; Robert C. Williamson; Iven M. Y. Mareels (Autom., 1998)

The Importance of Convexity in Learning with Squared Loss. - Wee Sun Lee; Peter L. Bartlett; Robert C. Williamson (IEEE Trans. Inf. Theory, 1998)

Structural Risk Minimization Over Data-Dependent Hierarchies. - John Shawe-Taylor; Peter L. Bartlett; Robert C. Williamson; Martin Anthony (IEEE Trans. Inf. Theory, 1998)

Entropy Numbers, Operators and Support Vector Kernels. - Robert C. Williamson; Alexander J. Smola; Bernhard Schölkopf (EuroCOLT, 1999)

On the poor robustness of sound equalization in reverberant environments. - Biljana D. Radlovic; Robert C. Williamson; Rodney A. Kennedy (ICASSP, 1999)

Nearfield broadband adaptive beamforming. - Thushara D. Abhayapala; Rodney A. Kennedy; Robert C. Williamson; Darren B. Ward (ISSPA, 1999)

An analysis of the exponentiated gradient descent algorithm. - Simon I. Hill; Robert C. Williamson (ISSPA, 1999)

Beamforming for a source located in the interior of a sensor array. - Darren B. Ward; Robert C. Williamson (ISSPA, 1999)

Support Vector Method for Novelty Detection. - Bernhard Schölkopf; Robert C. Williamson; Alexander J. Smola; John Shawe-Taylor; John C. Platt (NIPS, 1999)

The Entropy Regularization Information Criterion. - Alexander J. Smola; John Shawe-Taylor; Bernhard Schölkopf; Robert C. Williamson (NIPS, 1999)

Noise modeling for nearfield array optimization. - Thushara D. Abhayapala; Rodney A. Kennedy; Robert C. Williamson (IEEE Signal Process. Lett., 1999)

Finite sample properties of linear model identification. - Erik Weyer; Robert C. Williamson; Iven M. Y. Mareels (IEEE Trans. Autom. Control., 1999)

Entropy Numbers of Linear Function Classes. - Robert C. Williamson; Alexander J. Smola; Bernhard Schölkopf (COLT, 2000)

Error performance of a channel of known impulse response. - Paul D. Teal; Robert C. Williamson; Rodney A. Kennedy (ICASSP, 2000)

From Margin to Sparsity. - Thore Graepel; Ralf Herbrich; Robert C. Williamson (NIPS, 2000)

Regularization with Dot-Product Kernels. - Alexander J. Smola; Zoltán L. Óvári; Robert C. Williamson (NIPS, 2000)

New Support Vector Algorithms. - Bernhard Schölkopf; Alexander J. Smola; Robert C. Williamson; Peter L. Bartlett (Neural Comput., 2000)

Equalization in an acoustic reverberant environment: robustness results. - Biljana D. Radlovic; Robert C. Williamson; Rodney A. Kennedy (IEEE Trans. Speech Audio Process., 2000)

Constant Directivity Beamforming. - Darren B. Ward; Rodney A. Kennedy; Robert C. Williamson (Microphone Arrays, 2001)

Very low voltage power conversion. - Marshall Shepard; Robert C. Williamson (ISCAS (3), 2001)

Kernel Machines and Boolean Functions. - Adam Kowalczyk; Alexander J. Smola; Robert C. Williamson (NIPS, 2001)

Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms. - Robert E. Mahony; Robert C. Williamson (J. Mach. Learn. Res., 2001)

Regularized Principal Manifolds. - Alexander J. Smola; Sebastian Mika; Bernhard Schölkopf; Robert C. Williamson (J. Mach. Learn. Res., 2001)

Estimating the Support of a High-Dimensional Distribution. - Bernhard Schölkopf; John C. Platt; John Shawe-Taylor; Alexander J. Smola; Robert C. Williamson (Neural Comput., 2001)

Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. - Robert C. Williamson; Alexander J. Smola; Bernhard Schölkopf (IEEE Trans. Inf. Theory, 2001)

Convergence of exponentiated gradient algorithms. - Simon I. Hill; Robert C. Williamson (IEEE Trans. Signal Process., 2001)

Large Margin Classification for Moving Targets. - Jyrki Kivinen; Alexander J. Smola; Robert C. Williamson (ALT, 2002)

Agnostic Learning Nonconvex Function Classes. - Shahar Mendelson; Robert C. Williamson (COLT, 2002)

Particle filter beamforming for acoustic source localization in a reverberant environment. - Darren B. Ward; Robert C. Williamson (ICASSP, 2002)

Hyperkernels. - Cheng Soon Ong; Alexander J. Smola; Robert C. Williamson (NIPS, 2002)

Algorithmic Luckiness. - Ralf Herbrich; Robert C. Williamson (J. Mach. Learn. Res., 2002)

Covering numbers for support vector machines. - Ying Guo; Peter L. Bartlett; John Shawe-Taylor; Robert C. Williamson (IEEE Trans. Inf. Theory, 2002)

Exploiting sparsity in adaptive filters. - Richard K. Martin; William A. Sethares; Robert C. Williamson; C. Richard Johnson Jr. (IEEE Trans. Signal Process., 2002)

Acoustic beamforming exploiting directionality of human speech sources. - Terence Betlehem; Robert C. Williamson (ICASSP (5), 2003)

Channel equalization and the Bayes point machine. - Edward Harrington; Jyrki Kivinen; Robert C. Williamson (ICASSP (4), 2003)

Experimental comparison of particle filtering algorithms for acoustic source localization in a reverberant room. - Eric A. Lehmann; Darren B. Ward; Robert C. Williamson (ICASSP (5), 2003)

Loop removal from LDPC codes. - James A. McGowan; Robert C. Williamson (ITW, 2003)

Online Bayes Point Machines. - Edward Harrington; Ralf Herbrich; Jyrki Kivinen; John C. Platt; Robert C. Williamson (PAKDD, 2003)

Particle filtering algorithms for tracking an acoustic source in a reverberant environment. - Darren B. Ward; Eric A. Lehmann; Robert C. Williamson (IEEE Trans. Speech Audio Process., 2003)

Online learning with kernels. - Jyrki Kivinen; Alexander J. Smola; Robert C. Williamson (IEEE Trans. Signal Process., 2004)

Learnability of Probabilistic Automata via Oracles. - Omri Guttman; S. V. N. Vishwanathan; Robert C. Williamson (ALT, 2005)

Learning the Kernel with Hyperkernels. - Cheng Soon Ong; Alexander J. Smola; Robert C. Williamson (J. Mach. Learn. Res., 2005)

Particle Filter Design Using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environments. - Eric A. Lehmann; Robert C. Williamson (EURASIP J. Adv. Signal Process., 2006)

The Need for Open Source Software in Machine Learning. - Sören Sonnenburg; Mikio L. Braun; Cheng Soon Ong; Samy Bengio; Léon Bottou; Geoffrey Holmes 001; Yann LeCun; Klaus-Robert Müller; Fernando Pereira 003; Carl Edward Rasmussen; Gunnar Rätsch; Bernhard Schölkopf; et al. (J. Mach. Learn. Res., 2007)

Correction to "The Importance of Convexity in Learning With Squared Loss". - Wee Sun Lee; Peter L. Bartlett; Robert C. Williamson (IEEE Trans. Inf. Theory, 2008)

Generalised Pinsker Inequalities. - Mark D. Reid; Robert C. Williamson (COLT, 2009)

Surrogate regret bounds for proper losses. - Mark D. Reid; Robert C. Williamson (ICML, 2009)

Composite Binary Losses. - Mark D. Reid; Robert C. Williamson (J. Mach. Learn. Res., 2010)

Convexity of Proper Composite Binary Losses. - Mark D. Reid; Robert C. Williamson (AISTATS, 2010)

Information, Divergence and Risk for Binary Experiments. - Mark D. Reid; Robert C. Williamson (J. Mach. Learn. Res., 2011)

The Convexity and Design of Composite Multiclass Losses. - Mark D. Reid; Robert C. Williamson; Peng Sun 011 (ICML, 2012)

Mixability in Statistical Learning. - Tim van Erven; Peter D. Grünwald; Mark D. Reid; Robert C. Williamson (NIPS, 2012)

Strategy-Proof Prediction Markets - Ayman Ghoneim; Robert C. Williamson (CoRR, 2012)

Mixability is Bayes Risk Curvature Relative to Log Loss. - Tim van Erven; Mark D. Reid; Robert C. Williamson (J. Mach. Learn. Res., 2012)

Divergences and Risks for Multiclass Experiments. - Dario García-García; Robert C. Williamson (COLT, 2012)

Clustering: Science or Art? - Ulrike von Luxburg; Robert C. Williamson; Isabelle Guyon (ICML Unsupervised and Transfer Learning, 2012)

Loss Functions. - Robert C. Williamson (Empirical Inference, 2013)

Bayes-Optimal Scorers for Bipartite Ranking. - Aditya Krishna Menon; Robert C. Williamson (COLT, 2014)

On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems. - Harish G. Ramaswamy; Balaji Srinivasan Babu; Shivani Agarwal 001; Robert C. Williamson (COLT, 2014)

Elicitation and Identification of Properties. - Ingo Steinwart; Chloé Pasin; Robert C. Williamson; Siyu Zhang (COLT, 2014)

The Geometry of Losses. - Robert C. Williamson (COLT, 2014)

From Stochastic Mixability to Fast Rates. - Nishant A. Mehta; Robert C. Williamson (NIPS, 2014)

Generalised Mixability, Constant Regret, and Bayesian Updating. - Mark D. Reid; Rafael M. Frongillo; Robert C. Williamson (CoRR, 2014)

Exp-Concavity of Proper Composite Losses. - Parameswaran Kamalaruban; Robert C. Williamson; Xinhua Zhang (COLT, 2015)

Generalized Mixability via Entropic Duality. - Mark D. Reid; Rafael M. Frongillo; Robert C. Williamson; Nishant A. Mehta (COLT, 2015)

Learning with Symmetric Label Noise: The Importance of Being Unhinged. - Brendan van Rooyen; Aditya Krishna Menon; Robert C. Williamson (NIPS, 2015)

A Theory of Feature Learning. - Brendan van Rooyen; Robert C. Williamson (CoRR, 2015)

Learning in the Presence of Corruption. - Brendan van Rooyen; Robert C. Williamson (CoRR, 2015)

Fast rates in statistical and online learning. - Tim van Erven; Peter D. Grünwald; Nishant A. Mehta; Mark D. Reid; Robert C. Williamson (J. Mach. Learn. Res., 2015)

A Modular Theory of Feature Learning. - Daniel McNamara; Cheng Soon Ong; Robert C. Williamson (CoRR, 2016)

Bipartite Ranking: a Risk-Theoretic Perspective. - Aditya Krishna Menon; Robert C. Williamson (J. Mach. Learn. Res., 2016)

Composite Multiclass Losses. - Robert C. Williamson; Elodie Vernet; Mark D. Reid (J. Mach. Learn. Res., 2016)

f-GANs in an Information Geometric Nutshell. - Richard Nock; Zac Cranko; Aditya Krishna Menon; Lizhen Qu; Robert C. Williamson (NIPS, 2017)

The cost of fairness in classification. - Aditya Krishna Menon; Robert C. Williamson (CoRR, 2017)

Provably Fair Representations. - Daniel McNamara; Cheng Soon Ong; Robert C. Williamson (CoRR, 2017)

A Theory of Learning with Corrupted Labels. - Brendan van Rooyen; Robert C. Williamson (J. Mach. Learn. Res., 2017)

The cost of fairness in binary classification. - Aditya Krishna Menon; Robert C. Williamson (FAT, 2018)

Constant Regret, Generalized Mixability, and Mirror Descent. - Zakaria Mhammedi; Robert C. Williamson (NeurIPS, 2018)

Minimax Lower Bounds for Cost Sensitive Classification. - Parameswaran Kamalaruban; Robert C. Williamson (CoRR, 2018)

Costs and Benefits of Fair Representation Learning. - Daniel McNamara; Cheng Soon Ong; Robert C. Williamson (AIES, 2019)

Lossless or Quantized Boosting with Integer Arithmetic. - Richard Nock; Robert C. Williamson (ICML, 2019)

Fairness risk measures. - Robert C. Williamson; Aditya Krishna Menon (ICML, 2019)

A Primal-Dual link between GANs and Autoencoders. - Hisham Husain; Richard Nock; Robert C. Williamson (NeurIPS, 2019)

Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds. - Hisham Husain; Richard Nock; Robert C. Williamson (CoRR, 2019)

PAC-Bayesian Bound for the Conditional Value at Risk. - Zakaria Mhammedi; Benjamin Guedj; Robert C. Williamson (NeurIPS, 2020)

Assessing AI Fairness in Finance. - Lachlan McCalman; Daniel Steinberg; Grace Abuhamad; Marc-Etienne Brunet; Robert C. Williamson; Richard S. Zemel (Computer, 2022)

What killed the Convex Booster ? - Yishay Mansour; Richard Nock; Robert C. Williamson (CoRR, 2022)

Fairness and Randomness in Machine Learning: Statistical Independence and Relativization. - Rabanus Derr; Robert C. Williamson (CoRR, 2022)

On the Richness of Calibration. - Benedikt Höltgen; Robert C. Williamson (FAccT, 2023)

Random Classification Noise does not defeat All Convex Potential Boosters Irrespective of Model Choice. - Yishay Mansour; Richard Nock; Robert C. Williamson (ICML, 2023)

The set structure of precision. - Rabanus Derr; Robert C. Williamson (ISIPTA, 2023)

Towards a strictly frequentist theory of imprecise probability. - Christian Fröhlich; Rabanus Derr; Robert C. Williamson (ISIPTA, 2023)

Insights From Insurance for Fair Machine Learning: Responsibility, Performativity and Aggregates. - Christian Fröhlich; Robert C. Williamson (CoRR, 2023)

A General Framework for Learning under Corruption: Label Noise, Attribute Noise, and Beyond. - Laura Iacovissi; Nan Lu; Robert C. Williamson (CoRR, 2023)

The Geometry and Calculus of Losses. - Robert C. Williamson; Zac Cranko (J. Mach. Learn. Res., 2023)

Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity. - Christian Fröhlich; Robert C. Williamson (Trans. Mach. Learn. Res., 2023)

The Geometry of Mixability. - Armando J. Cabrera Pacheco; Robert C. Williamson (Trans. Mach. Learn. Res., 2023)

Systems of Precision: Coherent Probabilities on Pre-Dynkin Systems and Coherent Previsions on Linear Subspaces. - Rabanus Derr; Robert C. Williamson (Entropy, 2023)

Insights From Insurance for Fair Machine Learning. - Christian Fröhlich; Robert C. Williamson (FAccT, 2024)

Four Facets of Forecast Felicity: Calibration, Predictiveness, Randomness and Regret. - Rabanus Derr; Robert C. Williamson (CoRR, 2024)

Geometry and Stability of Supervised Learning Problems. - Facundo Mémoli; Brantley Vose; Robert C. Williamson (CoRR, 2024)

An Axiomatic Approach to Loss Aggregation and an Adapted Aggregating Algorithm. - Armando J. Cabrera Pacheco; Rabanus Derr; Robert C. Williamson (CoRR, 2024)

Limits to Predicting Online Speech Using Large Language Models. - Mina Remeli; Moritz Hardt; Robert C. Williamson (CoRR, 2024)

Causal modelling without counterfactuals and individualised effects. - Benedikt Höltgen; Robert C. Williamson (CoRR, 2024)

Five reasons against assuming a data-generating distribution in Machine Learning. - Benedikt Höltgen; Robert C. Williamson (CoRR, 2024)

Scoring Rules and Calibration for Imprecise Probabilities. - Christian Fröhlich; Robert C. Williamson (CoRR, 2024)

Strictly frequentist imprecise probability. - Christian Fröhlich; Rabanus Derr; Robert C. Williamson (Int. J. Approx. Reason., 2024)

Risk Measures and Upper Probabilities: Coherence and Stratification. - Christian Fröhlich; Robert C. Williamson (J. Mach. Learn. Res., 2024)

Information Processing Equalities and the Information-Risk Bridge. - Robert C. Williamson; Zac Cranko (J. Mach. Learn. Res., 2024)

Three Types of Calibration with Properties and their Semantic and Formal Relationships. - Rabanus Derr; Jessie Finocchiaro; Robert C. Williamson (CoRR, 2025)